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Karabila, I.; Darraz, N.; El-Ansari, A.; Alami, N.; El Mallahi, M. Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis. Encyclopedia. Available online: (accessed on 21 June 2024).
Karabila I, Darraz N, El-Ansari A, Alami N, El Mallahi M. Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis. Encyclopedia. Available at: Accessed June 21, 2024.
Karabila, Ikram, Nossayba Darraz, Anas El-Ansari, Nabil Alami, Mostafa El Mallahi. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis" Encyclopedia, (accessed June 21, 2024).
Karabila, I., Darraz, N., El-Ansari, A., Alami, N., & El Mallahi, M. (2023, August 17). Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis. In Encyclopedia.
Karabila, Ikram, et al. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis." Encyclopedia. Web. 17 August, 2023.
Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes.

sentiment analysis recommender system deep learning collaborative filtering ensemble learning

1. Introduction

The abundance of information available on the internet has created a challenge for users in sorting through and making decisions among the many options available for services such as restaurants, products, and hotels. This issue, known as information overload, can complicate the decision-making process [1]. To address this problem, recommendation systems have been developed to filter information and provide customized recommendations to users based on their specific tastes and preferences [2]. The goal of these systems is to minimize the time users spend searching for information and suggest items they may not have otherwise considered, thereby improving the quality of information access services.
The evolution of e-commerce websites has emphasized the value of recommendation systems in helping customers discover products that are relevant to their needs and preferences. RS has proven to be a useful tool in this context [3].
Several techniques are used for deciding which items to recommend in RSs, with the three most common techniques being collaborative filtering (CF), content-based filtering (CBF), and hybrid methods combining both [4]. CF is widely used and can be found on the majority of online shopping sites [5]. CF systems provide recommendations to a particular user based on the preferences and tastes of other users. These systems can be divided into two types: memory-based (MRB) and model-based (MB). The MRB method utilizes the similarity between items or users to retrieve information for the target user and make recommendations based on the obtained results [6]. The MB method builds a model to predict the ratings or preferences of the target user for a certain item and makes recommendations based on the estimated ratings. It includes two techniques: user-based and item-based [7]. In contrast, the CBF approach compares the semantic content of items [8]. The hybrid technique combines two or more recommendation algorithms or components into an RS [9]. However, traditional RS methods primarily depend on a single standard rating (overall score) for the recommendation process, which is usually insufficient for precise recommendations, as the overall score cannot provide a detailed analysis of the user’s behavior [10]. Furthermore, CF faces two main problems, sparsity and gray sheep [11], which make this method unreliable in some recommender systems. This motivates further research aimed at discovering practical solutions to improve the effectiveness of RS.
Recently, customer reviews have had a significant impact on customers’ decisions to use a service or purchase a product. Many consumers rely on the opinions of others when making decisions, leading to a substantial rise in the number of online customer reviews. Each review reflects the customer’s experience with a particular service, such as watching a movie, buying a product, or booking a room. In this regard, Sentiment Analysis methods can be utilized to deduce the customer’s emotions and opinions on various topics [12].
The objective of SA is to determine the emotional tone or attitude expressed in user-generated text related to a specific topic or entity [13]. This is achieved by automatically identifying and extracting information about the discussed entity and assessing whether the language used in the text conveys a negative, positive, or neutral sentiment. SA can be performed at three levels of data extraction [14]: aspect, sentence, and document level. There are three main approaches for solving the SA problem [15]: Lexicon-based methods, Machine Learning-based methods, and Hybrid methods. Lexicon-based methods were the first to be employed for SA. They rely on lexicons and linguistic rules and can be classified into two types: corpus-based and dictionary-based [16]. Machine Learning (ML)-based techniques include traditional and deep learning (DL) methods [17]. Finally, a hybrid approach combines lexicons and machine learning techniques [13]. The application of Deep Learning methods has been shown to be more effective than conventional approaches in sentiment analysis [18]. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Neural Networks (DNNs) can be utilized for sentiment classification.
It is common to integrate sentiment analysis methods in recommendation systems to enhance the quality and performance of recommendations [19]. Integrating SA into RS allows for gathering more information about user feedback and preferences on items, which can improve the effectiveness of the system.

2. Sentiment Analysis in Recommender Systems

In recent years, researchers have been actively exploring ways to enhance traditional collaborative filtering techniques in order to overcome challenges such as data sparsity, cold start, and the gray sheep problem. One promising approach to improving collaborative filtering is integrating sentiment analysis into the recommendation process. This section will discuss several relevant studies that have employed sentiment analysis in recommender systems, showcasing their unique contributions.
For example, in [20], Rayan et al. presented a combined learning technique that blends collaborative filtering and CBF for providing customized recommendations for personal well-being services. The research aims to overcome the limitations of conventional collaborative filtering, particularly in the situation of the cold-start issue. The proposed technique is tested using a dataset of personal well-being services and the results show that it surpasses traditional CF techniques.
Another study by the authors of [21] introduced a hybrid recommendation system that integrated CBF and collaborative filtering using SA based on the Naive Bayes algorithm. The system leveraged microblogging data to enhance the recommendation process. By considering the sentiment expressed in users’ feedback and preferences, the system generated more accurate and personalized recommendations.
The paper [22] makes a significant contribution to the field of recommender systems by introducing a novel sentiment-based model that tackles the issue of sparse data. Their contribution lies in demonstrating the effectiveness of incorporating sentiment analysis techniques into collaborative filtering-based recommendation algorithms. By leveraging textual reviews, their model enhances the performance of recommender systems by considering the sentiment expressed in user feedback. This integration of sentiment analysis provides valuable insights into improving user satisfaction and the overall quality of recommendations.
Osman et al. [23] developed a recommender system that incorporated contextual sentiment analysis to improve the precision and personalization of recommendations. By considering the context of users’ feedback and preferences, the system generated more relevant recommendations. This approach addressed the limitations of traditional collaborative filtering methods, which often overlooked the contextual information associated with users’ feedback.
To address the cold-start problem, sentiment analysis of textual data from online communities, such as Twitter and Facebook, has been employed. In one study [24], the authors proposed a method that integrated sentiment analysis of social network data to enhance the precision and personalization of recommendations. They utilized machine learning methods, including Support Vector Machine (SVM) and Naive Bayes (NB), to perform sentiment analysis and gather additional information about new users or items.
Ziani et al. [25] introduced a multilingual recommendation system that combined user-based collaborative filtering with sentiment analysis. The system utilized a semi-supervised SVM as a sentiment classification technique to analyze the sentiment of reviews and ratings provided by users. By considering both user preferences and sentiment feedback, the system generated highly accurate and personalized recommendations. The multilingual approach catered to users who spoke different languages, making the system more inclusive and accessible.
In another study [26], collaborative filtering was combined with sentiment analysis to enhance the performance of a recommender system for groups of users. The authors employed classification methods, such as NBM and Linear Support Vector Classification (LSVC), for sentiment analysis. Additionally, Singular Value Decomposition (SVD) was used to improve the scalability of the recommender system. The findings demonstrated that the proposed technique improved the effectiveness of the system, providing more accurate and personalized recommendations.
In a different approach, authors in [27] adopted a novel method to enhance the effectiveness of collaborative filtering algorithms. They utilized lexicon-based sentiment analysis to incorporate the emotional content present in the recommended items, resulting in a more precise prediction of users’ preferences.
The work [28] addresses the limitations of traditional CF techniques by introducing a sentiment digitization modeling framework. The authors emphasize the importance of considering user sentiment in recommendation systems, as it can significantly impact the relevance and personalization of recommendations. They argue that conventional methods often overlook the nuanced emotional aspects of user preferences, leading to suboptimal recommendations. To overcome this limitation, the authors propose a sentiment digitization modeling technique that effectively captures and quantifies the sentiment expressed in user feedback. The framework leverages sentiment analysis algorithms to transform the qualitative sentiment information into quantitative scores, which can be incorporated into the recommendation process. The proposed approach is evaluated using a real-world dataset, and the results demonstrate improved recommendation performance compared to traditional CF methods. The study highlights the potential of sentiment digitization modeling in enhancing recommendation systems by considering the emotional aspect of user preferences.
The work by Devipriya et al. [29] highlights the effectiveness of different deep learning architectures, specifically RNN and CNN, for recommendations in SA in social applications. The study shows that the RNN architecture demonstrates a better understanding of the relationships between words and achieves improved performance in sentiment label training. On the other hand, the CNN architecture initially struggles with phrase-level labels but can be enhanced by leveraging pre-trained word2vec vectors to address overfitting and enhance performance. The findings of the study imply that deep learning techniques can effectively analyze sentiments in social applications. By utilizing these architectures, recommender systems can be built with improved accuracy and effectiveness in providing recommendations based on user sentiments. The research also suggests potential avenues for further improvements and applications in the field of building recommender systems for various socially relevant domains.
RSs typically depend on explicit user ratings, but this approach becomes impractical in many domains. Additionally, even when explicit ratings are available, their trustworthiness and reliability can pose limitations to the recommender system. To overcome these challenges, analyzing sentiments within textual data, such as reviews and comments, can provide valuable implicit feedback alongside traditional ratings. This approach proves beneficial in improving the accuracy of recommendations for users, especially when there is a large volume of text-based feedback available. While previous studies have incorporated sentiment analysis into recommendation methods, most of them have utilized conventional sentiment techniques.


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